1 This publication presents data from the Survey of Tourist Accommodation (STA). The STA is a census of all in-scope accommodation establishments within Australia. This release includes the four quarters of the 2015-16 financial year, that is September quarter 2015, December quarter 2015, March quarter 2016 and June quarter 2016.

SCOPE
2 Establishments within the scope of the survey provide predominantly short-term non-residential accommodation, i.e. accommodation which is not leased, and which is provided to guests who would generally stay for periods of less than two months. Some of these establishments also provide long-term residential accommodation. The amount of such activity is considered to be insignificant and is included in the data presented in this publication.

3 Establishments in scope of the STA are:

hotels and resorts with 15 or more rooms

motels, private hotels and guest houses with 15 or more rooms

serviced apartments with 15 or more units.

COVERAGE
4 For the 2015-16 collection period, the annual frame update process was undertaken using a file provided by STR Global, a company that tracks supply and demand data for the hotel industry. The update process was routine for 2015-16.

5 The 2014-15 frame update process resulted in the identification and subsequent addition of 279 new establishments to the STA beginning with the September quarter 2014. The addition of the 279 tourism establishments resulted in a break in time series between the June and September quarters 2014. The impact of the break in time series is explained in the 2014-15 STA Technical note.

6 During processing of 2015-16 STA data, it became clear that 40 accommodation establishments added during the 2014-15 frame maintenance exercise were duplicates of establishments already on the frame. As a consequence the 2014-15 STA data was overstated. In comparing estimates in original terms, it is recommended that users read the Technical note included in this release for interpreting the movements in the data between 2014-15 and 2015-16.
ACCOMMODATION CLASS
7 Data by Accommodation class for states and territories are included in this publication. Accommodation class data has replaced star gradings data (known as star ratings by industry) , however the star grading categories can be fully mapped to the Accommodation class categories. The Accommodation class mappings are:

Budget: one and two star rated establishments or equivalent

Mid scale: three star rated establishments or equivalent

Upscale: four star rated establishments or equivalent

Luxury: five star rated establishments or equivalent

STATISTICAL GEOGRAPHY
8 Small area statistics for 2015-16 are classified to the Australian Statistical Geography Standard (ASGS): Volume 3 - Non ABS Structures, 2015 Edition (cat. no. 1270.0.55.003) effective from September quarter 2015.
9 Small area data (SA2) are aggregated to tourism regions as defined by relevant state and territory tourism organisations. Tourism regions are reviewed annually and are subject to boundary and name changes. Where changes have occurred, care should be taken when making comparisons with previously published data at this level.
10 Data by tourism regions and small area (SA2) are included in this publication and can be located in the downloads section. Small area data up to and including June quarter 2013 are available in Tourist Accommodation, Small Area Data (cat. no. 8635.0.55.002 for national data and cat. no. 8635.1.55.001 - 8635.8.55.001 for state/territory data).

11 Details of the composition of tourism regions and maps of tourism regions are provided in the ABS publication Tourism Region Maps and Allocation File (cat. no. 9503.0.55.001) available from the ABS web site <www.abs.gov.au>.

DATA QUALITY

12 The survey does not have a sample component and the data are not subject to sampling variability. However, other inaccuracies collectively referred to as non-sampling error may affect the data. These non-sampling errors may arise from a number of sources, including:

errors in the reporting of data by providers

errors in the process of capturing data

imputation for missing data

definition and classification errors

incomplete coverage.

13 Every effort has been made to reduce non-sampling error to a minimum by careful design and testing of questionnaires, and efficient operating procedures and systems used to compile statistics.

Response rates
14 The quality and reliability of survey data can be affected by the degree of response to a survey however, it is rare to achieve a 100% response rate for any survey. The response rates for the Survey of Tourist Accommodation at state level are shown below.

RESPONSE RATES(a), Hotels, Motels and Serviced apartments

2013-14

2014-15

2015-16

%

%

%

NSW

87.1

86.9

88.2

Vic.

88.9

89.4

90.6

Qld

87.8

88.9

88.9

SA

88.6

90.3

91.2

WA

86.4

87.5

93.4

Tas.

90.5

94.8

94.7

NT

93.3

89.8

88.5

ACT

88.2

88.5

86.0

Aust.

88.0

88.5

89.6

(a) Only one response rate is available for the financial year as the collection of data for the four quarters is carried out at one time at the end of the period.

Imputation rates
15 Missing data items are replaced by imputed values based on reported data. Average quarterly movements are applied to previously reported data for each non-responding unit to estimate values for missing data items. Only if previously reported data are not available, will data from a similar unit be used as a 'donor' for the missing data items.
16 The imputation rates for Room nights occupied and Takings from accommodation for the most recent quarters at a national level are shown below.

IMPUTATION RATES, Room nights occupied

Sep Qtr 2014

Dec Qtr 2014

Mar Qtr 2015

Jun Qtr 2015

Sep Qtr 2015

Dec Qtr 2015

Mar Qtr 2016

Jun Qtr 2016

Activity

%

%

%

%

%

%

%

%

Licensed hotels and resorts

11.3

11.2

11.0

11.1

7.8

7.4

7.2

7.6

Motels, private hotels and guest houses

16.7

16.1

16.2

16.4

14.4

14.1

14.0

13.8

Serviced apartments

16.7

17.3

17.2

17.0

10.0

9.6

9.4

9.4

Hotels, motels and serviced apartments

14.3

14.3

14.2

14.2

10.4

10.0

9.8

9.9

IMPUTATION RATES, Takings from accommodation

Sep Qtr 2014

Dec Qtr 2014

Mar Qtr 2015

Jun Qtr 2015

Sep Qtr 2015

Dec Qtr 2015

Mar Qtr 2016

Jun Qtr 2016

Activity

%

%

%

%

%

%

%

%

Licensed hotels and resorts

10.3

10.1

9.8

10.1

7.7

7.2

7.0

7.5

Motels, private hotels and guest houses

15.9

15.2

15.4

15.6

13.2

13.0

12.8

12.9

Serviced apartments

16.5

17.5

17.3

16.7

9.5

9.0

8.9

8.9

Hotels, motels and serviced apartments

13.4

13.4

13.2

13.3

9.5

9.0

8.8

9.1

SEASONAL ADJUSTMENT
17 Seasonal adjustment is a means of removing the estimated effects of normal seasonal variation from the original time series so that the effect of other influences on the series may be more clearly recognised. Seasonal adjustment procedures do not aim to remove the irregular or non-seasonal influences which may be present in any particular quarter. Irregular influences that are highly volatile can make it difficult to interpret the movement of the series even after adjustment for seasonal variation, and cannot be assumed to indicate changes in the trend.

18 While the Concurrent method of seasonal adjustment is used, the seasonally adjusted and trend series have been revised following the annual review of the seasonal adjustment on data up to June quarter 2015. Since the collection frequency of the STA moved from quarterly to annual on a financial year basis from 1 July 2013, the annual review was performed to quality assure the seasonal adjustment process. As a result, the seasonally adjusted and trend estimateshave been revised.
19 The Survey of Tourist Accommodation collection uses Autoregressive Integrated Moving Average (ARIMA) modelling techniques for the majority of applicable time series. The revision properties of the seasonally adjusted and trend estimates can be improved by the use of ARIMA modelling. ARIMA modelling relies on the characteristics of the series being analysed to project future period data. The projected values are temporary, intermediate values, that are only used internally to improve the estimation of the seasonal factors. The projected data do not affect the original estimates and are discarded at the end of the seasonal adjustment process.
20 For more information on the details of ARIMA modelling see the feature article 'Use of ARIMA modelling to reduce revisions' in the October 2004 issue of Australian Economic Indicators (cat. no. 1350.0). Any queries regarding the ARIMA modelling should be directed to Time Series Analysis on (02) 6252 6345 or email <time.series.analysis@abs.gov.au>.
21 Unreliable seasonal adjustment: In using the seasonally adjusted series, care should be exercised for the following data series: Takings, Australian Capital Territory because of the difficulties associated with reliably estimating the seasonal pattern. This series will be revised during the next annual seasonal review.

TREND ESTIMATES
22 Smoothing the seasonally adjusted series reduces the impact of the irregular component of the seasonally adjusted series and creates the trend estimates. The trend estimates are derived by applying a 7-term Henderson moving average to the quarterly seasonally adjusted series. The Henderson moving average used in the middle of the time series is symmetric but, as the end of a time series is approached, asymmetric forms of the symmetric moving average are applied. Unlike the weights of the symmetric 7-term Henderson moving average, the asymmetric weights have been tailored to suit the particular characteristics of individual series.
23 While these techniques enable trend estimates for the latest period to be produced, the process does result in revisions to the trend estimates in recent quarters, particularly as additional original estimates become available. For further information refer to Information Paper: A Guide to Interpreting Time Series - Monitoring Trends, 2003 (cat. no. 1349.0) available at the ABS web site <www.abs.gov.au> or contact Time Series Analysis on (02) 6252 6345 or email <time.series.analysis@abs.gov.au>.

CONFIDENTIALISATION OF DATA
24 Under the Census and Statistics Act, when releasing statistics the ABS is required to do this in a manner that is "not likely" (in a legal sense) to enable the identification of a particular person or organisation. A number of techniques are used to do this, including suppression of information. To ensure provider confidentiality in the Survey of Tourist Accommodation, the ABS uses a computerised process known as Disclosure Avoidance Analysis System (DAAS) to confidentialise the entire tourist accommodation dataset each quarter. This process not only ensures that data are suppressed to ensure individual establishments cannot be identified, but also suppresses data in other (consequential) cells to ensure data cannot be derived through deduction from the information available.
USER AGGREGATION OF DATA
25 The aggregation of data by users across time periods should be undertaken with caution, due to the possibility of non-inclusion of confidentialised data (see the above section for more information about confidentialisation). Where one or more cells contributing to a total have been confidentialised (ie, contains the value of n.p.), the resulting aggregated total will be incorrect. However, some broader levels of data may not be affected by confidentialised cells.
26 Where data can be aggregated (ie, no confidentialised cells are included) for calendar and financial year/s purposes, the data items Establishments, Rooms and Bed spaces should not be aggregated. For these items it is recommended that for calendar years, the value of the December quarter is used, and for financial years, the value of the June quarter is used.
27 Any data items that have been derived from other items collected in the survey cannot be aggregated (ie, all those with labels ending in 'rate' or commencing with 'average'). These items must be re-derived based on the aggregation of each of the quarterly items collected in the survey used in the derivation of the rate or average (see Glossary for formulas).
28 Users are cautioned against deriving any non-standard aggregations (eg, aggregation of selected geographical areas such as capital city areas and balance of state; aggregation of selected activities such as hotels and motels combined). This is because data are confidentialised based on the standard data item structure.
EFFECTS OF ROUNDING
29 Where figures have been rounded, discrepancies may occur between totals and the sum of the component items.
30 Estimates of movement shown in this publication are obtained by taking the difference of unrounded estimates. The movement is then rounded to one decimal place. Therefore where a discrepancy occurs between the reported movement and the difference of the rounded estimates, the reported movement will be more accurate.